Method and system for providing information to a user relating to a point-of-interest

ABSTRACT

In methods and systems for providing information to a user relating to a point-of-interest (POI), a query is received via a communication network. A processor receives information relating to at least one POI based on the received query. The retrieved information comprises a set of retrieved POI tips relating to the POI. Each of the set of retrieved POI tips is previously extracted from POI free-text reviews and respectively associated in the data storage with one or more semantic categories based on a predefined tip ontology. The processor generates a user interface interactively displaying the retrieved POI tips and the semantic categories associated with each of the POI tips. The displayed POI tips are sorted by their associated displayed semantic categories. The generated user interface is transmitted to a computing device for display.

TECHNICAL FIELD

The present disclosure relates to methods and systems for interactivesearch using computing devices, and more particularly, to providing aninteractive search for points-of-interest (POIs).

BACKGROUND

It is known to provide map applications or other applications via acomputing device where a user can search for and locate informationregarding so-called points-of-interest (POIs). POIs include, but are notlimited to, restaurants, parks, museums, tourist attractions, historicaland cultural landmarks, hotels, etc.

For instance, there are many web and mobile applications dedicated tosearching and retrieving POIs, including generic map applications (e.g.,Google Maps, Bing, Naver Maps, etc.) and more specific websites orapplications (e.g., TripAdvisor, Booking.com, Foursquare.com, etc.).Such web and mobile applications allow users to search for POIs such asrestaurants, hotels, museums, parks, etc. In response to a search for aPOI, certain information is automatically retrieved and presented to theuser via a user interface.

However, the present inventors have discovered that certain relevantinformation related to searched POIs, including practical informationthat may be useful to either select a particular POI or assist withpreparing a visit to the POI, may either not be automatically retrievedand presented using conventional POI web and mobile applications, or maybe difficult to easily locate via a conventional POI search interface.Thus, users often need to use both the automatically generated resultsfrom conventional POI web and mobile applications and perform anadditional, manual search to locate such practical information, which isinefficient.

SUMMARY

According to one aspect of the disclosed embodiments, methods areprovided for providing information to a user relating to apoint-of-interest (POI). A query is received via a communicationnetwork. A processor receives information from a data storage relatingto at least one POI based on the received query, wherein the retrievedinformation comprises a set of retrieved POI tips relating to the atleast one POI. Each of the set of retrieved POI tips is previouslyextracted from POI free-text reviews and respectively associated in thedata storage with one or more semantic categories based on a predefinedtip ontology. The processor generates a user interface interactivelydisplaying the retrieved POI tips and the semantic categories associatedwith each of the retrieved POI tips, wherein the displayed POI tips aresorted by their associated displayed semantic categories. The generateduser interface is transmitted to a computing device of the user via thecommunication network for display on the computing device.

According to another aspect of the disclosed embodiments, a system forproviding information to a user relating to a point-of-interest (POI)includes a communication interface, a back-end including a processor,and a memory. The back-end includes a knowledge base, a tip identifiermodule, and an information retrieval module.

The tip identifier module is configured to identify tip occurrences ineach of a plurality of POI free-text reviews using a natural languageprocessing model, associate the identified tip occurrences with at leastone semantic category based on a predefined tip ontology using thenatural language processing model, and store the identified tipoccurrences and their association with the at least one semanticcategory in the knowledge base. The information retrieval module isconfigured to retrieve information from the knowledge base relating toat least one POI in response to a received query, wherein the retrievedinformation comprises a set of the identified tip occurrences relatingto the at least one POI and their respective associated semanticcategories.

A front-end of the system includes a processor and memory, and comprisesa POI query interface module, which is configured to receive the queryvia the communication interface, and a POI detail interface module whichis configured to generate and transmit a user interface interactivelydisplaying the retrieved POI tips and the semantic categories associatedwith each of the retrieved POI tips, wherein the displayed POI tips aresorted by their associated displayed semantic categories.

According to a complementary aspect, the present disclosure provides acomputer program product, comprising code instructions to execute amethod according to the previously described aspects; and acomputer-readable medium, on which is stored a computer program productcomprising code instructions for executing a method according to thepreviously described aspects.

DESCRIPTION OF THE DRAWINGS

The present disclosure will become more fully understood from thedetailed description and the accompanying drawings, wherein:

FIG. 1 illustrates an example of an example communication environment inwhich a method for providing information to a user relating to apoint-of-interest (POI) to one or more user computing devices accordingto the present disclosure may be performed;

FIG. 2 sets forth an example architecture for a system for providinginformation to a user relating to a point-of-interest (POI) methodaccording to the present disclosure;

FIG. 3 shows example semantic categories from an example tip ontologyand associated example tip occurrences provided in text snippets fromPOI free-text reviews;

FIG. 4 shows an example method for populating a POI knowledge base (KB)with POI tip occurrences from POI free-text reviews, according to aninventive embodiment;

FIG. 5 shows an example method for identifying and labeling tipoccurrences from POI free-text reviews using a natural languageprocessing model;

FIG. 6 shows an example method for presenting information to a userrelating to a point-of-interest (POI), including POI tip occurrences andassociated semantic categories, in response to a POI query, via a userinterface;

FIG. 7 shows an example screenshot for an expandable/collapsible userinterface portion including retrieved POI tips and semantic categoriesin response to a POI query;

FIG. 8 shows the expandable/collapsible user interface portion of FIG. 7, in which a displayed first semantic category is selected and expandedto display associated retrieved POI tip occurrences within textsnippets, where tip targets within the tip occurrences are emphasized bya unique (bold) font;

FIG. 9 shows the expandable/collapsible user interface portion of FIG. 7, in which a second semantic category is selected and expanded todisplay associated retrieved POI tip occurrences within text snippets,and where the first displayed semantic in FIG. 8 is collapsed; and

FIG. 10 shows an example method for retraining a natural languageprocessing model based on user feedback from displayed POI tipoccurrences.

In the drawings, reference numbers may be reused to identify similarand/or identical elements.

DETAILED DESCRIPTION

The present inventors have observed that POI information provided tousers via conventional web and mobile applications is often limited tothe content of particular POI knowledge bases (KBs). This POIinformation can include crowdsourced information (e.g., crowdsource tipsin online guides) or inferred information from user data (e.g., aninflux of visitors over time).

For example, conventional web and mobile applications for searching POIstypically provide access to two general types of information relating tothe POIs. One general type is sentiment-based evaluations; that is, theusers' subjective evaluations of the quality of a particular POI and itsvarious aspects or facets. Sentiment-based evaluations help users choosea POI according to how well its general quality and/or the qualities ofone or more of its aspects are (subjectively) rated. Such ratings areoften directly entered by users via an interface of the web and mobileapplications or an affiliated site.

The other general type of POI-related information that may be providedis factual and/or practical information about a particular POI.Nonlimiting examples of such information include the POI's address,geolocation, phone number, opening hours, how long a visit takes, howmuch it costs, etc. Factual and/or practical information helps usersselect a POI and/or prepare their visit appropriately to optimize theirexperience at the POI. This type of information is typically availablein POI knowledge bases (KBs), which can include traditional POIdatabases containing standard POI-related information (e.g., name,geolocation, address, phone number or other contact information, lengthof typical visit, cost, opening hours, representative image, etc.), aswell as crowdsourced information (e.g., Q/A results, or structured tipsin online guides), as well as inferred information from users' personaldata (e.g., a POI's “popular time” in a search engine).

In many cases, occurrences of this latter general type of POI-relatedinformation, providing additional and complementary items of usefulinformation that can help users select a POI and perhaps prepare avisit, can also be found separately, such as in POI free-text reviews.As used herein, “free-text” (i.e., free-form text as opposed tostructured text) means, in the context of POI reviews, textualinformation that may be freely entered by a reviewer that is notassociated with a pre-defined category of information (e.g., a ratingbetween one and five). Such items of useful information entered asfree-text, though, are not easily selected and made directly accessibleby conventional web or mobile POI search applications. Instead, usersare required to manually review (e.g., read) the POI free-text reviewsto locate these items. For example, in cases where there is only alimited amount of free-text that is displayed for each free-text reviewon a POI summary screen, users are required to individually select eachPOI free-text review in order to read the review in its entirety.

The information that is automatically provided by POI KBs and presentedto a user via a conventional POI result interface is thus believed to beincomplete and/or cumbersome to access. Such automatically providedinformation particularly lacks a significant amount of the practicaland/or factual information that users (potential visitors) may want toknow to select and/or prepare a visit for a POI.

Coverage and presentation of conventional POI search results highlyskews toward POI coverage of reviews (e.g., user reviews) as opposed to,say, crowdsourced tips and Q/A results. For instance, an example POIinformation result found by the present inventors in Google Maps for thePOI Grotte de Choranche as of Nov. 15, 2018 included only elevenanswered questions, while there were over 1066 reviews. Although many ofthese POI free-text reviews were short and did not contain any tips, theoverwhelmingly large number of reviews as compared to the answeredquestions illustrated that these POI free-text reviews still includedbetter POI tip coverage than the crowdsourced Q/A or tips.

Example tips that were found in such POI free-text reviews included:accessibility for disabled visitors, or parents with strollers, etc.;visitor profiles, e.g., age range, occupation profile, families, etc.;what to wear and how to dress during the visit; what accessories anditems are necessary or helpful during the visit, e.g., ID or passport,food/water, sunblock, etc.; suitable visit periods/time, e.g.,best-suited season or day(s) of the week, weather-related time, etc.;cost/prices of the POI visit; payment modes; whether booking is suitableor required, and how to book; etc. However, as mentioned above, suchadditional tips are not made easily and quickly accessible to users.

According to example embodiments provided herein, POI search methods andsystems are provided that automatically identify and select items ofrelevant (e.g., practical and/or useful) POI information, referred toherein as tips, from POI free-text reviews (e.g., users' reviews ofparticular POIs). Further, example methods automatically group the tipsinto semantic categories and present the grouped tips to the user via auser interface. In this way, such items of relevant POI information canbe made quickly, directly, and clearly accessible to users when theysearch for and select POIs, such as via a web or mobile application.

An example system includes a back-end service that combines theautomatic identification of tips from the textual content of POI userreviews and the categorization of these tips into a predefined ontologyof suggestion types. The system further includes a front-end service tothe end-users (users) that allow them to quickly focus on, or searchfor, types of tips that interest them.

“Automatically” as used herein refers to example methods and systemsprocessing POI free-text reviews, including identifying and classifying(and, preferably, storing) the tips as provided herein, without firstrequiring a request or prompt from a user searching for a POI. Forexample, POI free-text reviews may be processed individually as they arecollected and/or stored in a data storage, such as a POI knowledge base(KB). Alternatively, POI free-text reviews may be processed in batcheson a regular (e.g., periodic) basis after previously being collected andstored. In either event, the tips generated by POI free-text reviews,associated with semantic categories, are stored in advance of a userquery for the associated POI so that the result presented to the userautomatically include these tips, sorted (or arranged) by theirassociated semantic categories.

By contrast, in conventional POI result interfaces, users can view somestandard POI information, and possibly view crowdsourced tips and Q/Aresults. However, to obtain the additional practical and usefulinformation that can be gleaned from POI free-text reviews, users mustessentially manually go through the POI free-text reviews and read them,which can be cumbersome and time-consuming.

In some conventional map applications, reviews can be ordered indifferent ways, including by so-called “relevance.” In other examplemethods, certain suggestions may be extracted from text, but a simplebinary decision (suggestion vs no suggestion) is made. Such reviews orsuggestions are not semantically grouped, and further it is not possibleto order them so that the reviews containing useful/practicalinformation come first. Instead, such useful/practical information(e.g., information about price, onsite facilities/services (toilets,restaurant, shop)), and other useful tips, are buried in the middle oreven the tail of a list or reviews so ordered by “relevance.”

“Tips” as used herein generally refer to any practical information in afactual mode (practical or fact-based suggestions), contained in reviewsof particular POIs provided in a free-text structure (“POI free-textreviews”), which can help users to select POIs, prepare a visit to POIs,etc. Generally, “tips” as used herein differ from, say, mere genericreviews or merely generic review aspects (that is, not specific to aPOI) in that tips provide suggestions to the users (readers) regarding aparticular POI. Further, tips differ from mere sentiment-basedstatements (e.g., subjective statements or opinions) in that tips arefactual. In other words, a “tip” is not provided by mere subjectiveassessments of the POIs, such as the quality of particular POIs or theirrespective aspects. Such subjective assessments may be provided in, forinstance, global or aspect-specific user rating scores, and/or takeninto account by aspect-based sentiment analysis systems for a POIsearch. For example, a text statement such as “Very nice park!” wouldnot be considered a “tip” as used herein. However, factual suggestionscan be both explicit, such as “bring warm clothing,” or implicit, suchas “online booking gets a 15% discount” (in both cases, no sentiment isinvolved, and objective, factual information can be extracted).

In some conventional POI services and applications, by contrast, alluser reviews and/or comments may be generally referred to as “tips” butare not considered tips as used in the present disclosure. For instance,a conventional POI service may include simple sentiment-basedstatements, or more general comments not specific to a particular POI,such as “It's with your feet that you move, but it's with your heartthat you dance!” As another example, personal comments provided on aconventional POI service such as “Close to my church” that would notassist a (more general) user in selecting a POI and/or preparing a visitis not considered a tip as used herein.

While some conventional POI applications automatically select anddisplay certain terms directly found in reviews, such terms do notconsider semantic categories that can help a user navigate semanticallyand quickly access useful tips. For example, for the POI Grotte deChoranche, directly selected terms from a text may include items such as“great tour,” “cave,” “cathedral,” “nature,” etc. Some of these termscan be irrelevant or even misleading, such as “cathedral” (there is nocathedral, although there is an area inside the cave called“cathedral”), or “light jacket” (a warm jacket is more advisable becauseit is quite cold inside the cave). More significantly, such directresults are typically incomplete, as many terms/phrases related touseful tips are omitted by such selection, such as price, accessibility,onsite facilities/services, etc. To determine whether suchdirectly-selected terms are relevant, accurate, or complete, a usersearching for information may need to read the full context of each termwithin each review themselves, which is time-consuming.

While it is possible for a user to manually search POI free-text reviewswith keywords to locate tips, this is a suboptimal solution, at leastbecause: 1) one has to run multiple distinct queries for varioussemantic categories of tips (e.g., to determine how to dress for aparticular POI, a user may have to provide queries for each of variousterms for manners of dressing, such as “outerwear,” “clothes,” etc.); 2)even for a single tip category (e.g., onsite facilities), one has toenter multiple queries or multiple terms (e.g., a query for “restroom”may not yield any results, while one for “toilet” does); or 3) reviewsfor a particular POI typically do not contain all categories of tips,which will not be known by the user in advance of searching, and thususers end up wasting time searching with multiple keywords and queriesfor tips that may be absent from the reviews.

Embodiments of the present provide, among other things, methods andsystems for providing information to a user relating to apoint-of-interest (POI) in which, in response to a query via acommunication network, a user interface displays POI tips retrieved froma data storage (such as but not limited a POI knowledge base (KB)),where such tips are: automatically selected from POI free-text reviews;automatically associated with one or more semantic categories based on apredefined tip ontology; and displayed in semantically categorized setsso that users can access them directly and selectively.

Presenting such information can be in addition to presenting so-calledstandard information and/or crowdsourced Q/A or tips conventionallyprovided by a retrieval from POI knowledge bases. “Crowdsourced tips” asused herein is intended to refer to structured tips that are createdmanually to fill in (populate) an explicit tip database, besides userreviews. Similarly, “Crowdsourced Q/A” is intended to refer to explicitquestions/answers, for instance those created by users, for POIs.

An example end-to-end system can include, for instance, a back-end thatstores a map with POIs and associated POI free-text reviews, where theback-end further analyzes the POI free-text reviews to extract tips andclassifies them into an ontology. A front-end then presents the resultsvia an interactive user interface for navigation, allowing a user toselect among semantic tip categories, and among individual tips withinthe semantic tip categories. The interactive user interface can alsoinclude an interface displaying matching POIs in response to a userquery, such as but not limited to a displayed map indicating matchingPOIs in response to the search. One or more matching POIs can then beselected to provide the user interface displaying the tips and semantictip categories, preferably along with other POI-related information suchas standard POI information.

In some example embodiments, the user can interact with tip results viathe user interface in additional ways beyond selecting/deselecting. Forexample, the user interface may allow a user to provide feedback such asby rating the usefulness of tips, and this feedback can then be used toimprove a tip identifier model.

In this way, when a user receives a point-of-interest (POI) in responseto a query (and, possibly, a selection of the particular POI among othermatching POIs), they can have quick access to structured data regardingthis POI, as well as quick access to different types of informationexpressed in users' reviews (tips) through a semantically categorizedmenu of tips information. Significantly, the tips are categorized insemantic categories that a user can navigate to access the type ofinformation that user is interested in. The user can thus directlyaccess such information without, say, needing to separately read throughuser reviews, or to review search terms with keywords.

Preferred embodiments will now be discussed with respect to thedrawings. The drawings include schematic figures that are not to scale,which will be fully understood by skilled artisans with reference to theaccompanying description. Features may be exaggerated for purposes ofillustration. From the preferred embodiments, artisans will recognizeadditional features and broader aspects of the invention.

FIG. 1 shows an example computing environment incorporating a system 20for providing information to a user relating to a point-of-interest(POI) to one or more user computing devices 22. The system includes acomputer, such as but not limited to a server computer (server) 24,which may be connected over a network 26 via one or more communicationinterfaces 28 to one or more user computing devices 22, which may, butneed not, act as clients.

A computer can be embodied in the server computer 24 as shown in FIG. 1, or may be embodied in a local computer, a client computer, a personalcomputer, a mobile communication device (e.g., a portable phone or othercomputing device, personal digital assistant, wearable device, embeddedprocessor, augmented reality device, virtual reality device, etc.), orany other suitable computing device that can be configured to performmethods disclosed herein, or any combination of computing devices.

The example network 26 may be embodied in one or more of a wirelessnetwork or a wired network, such as but not limited to a local areanetwork (LAN or WLAN), a wide area network (WAN or WWAN), the Internet,a telecommunications network such as a public switched telephone network(PSTN), landline network, Ethernet or fiber network, a radio networksuch as a frequency-hopping spread spectrum (FHSS) radio network, GPRSnetwork, Wi-Fi (e.g., 802.11) network, Wi-Max (e.g., 802.16) network,TCP/IP network, CDMA network, or network including any combination ofthe above.

Each of the communication interfaces 28 may be software and/or hardwareassociated in communicating to other devices. The communicationinterfaces 28 may be of one or different types that include a userinterface, USB, Ethernet, Wi-Fi, wireless, RF, optical, cellular, or anyother communication interface coupled to the network.

The computer 24 includes a processor 32, which may include one or moreof a general purpose processor, a special purpose processor, aconventional processor, a digital signal processor (DSP), a virtualprocessor executed by one or more additional processors, a plurality ofmicroprocessors operating in series or in parallel, one or moremicroprocessors in association with a DSP core, a controller, amicrocontroller, Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Array (FPGAs) circuits, any other type of integratedcircuit (IC), a state machine, and others. The processor 32 may performone or a combination of signal coding, data processing, power control,input/output processing, and/or any other functionality that enables thecomputer 24 and/or the system to operate in accordance with its intendedfunctionality. Reference to a “processor” herein is intended to likewiserefer to either a single processor, or to one or more processorsoperating in series and/or in parallel.

The computer 24 may further include a graphics interface, including agraphics processing unit (GPU) 34, video memory 36, and/or videointerface (not shown). These components may cooperate to displaygraphics and text on a video monitor, such as a display (not shown). AGPU 34 may additionally or alternatively provide all or a portion of aprocessor.

The processor 32 may receive, generate, and process data as provided inone or more methods disclosed herein. In operation, the processor 32 mayfetch, decode, and execute one or more instructions, and transferinformation to and from other resources via a transfer path of thecomputer, such as a system bus 38. An example system bus 38 connectsexample components in the computer 24 and can define the medium for dataexchange. A system bus 38 typically includes data lines for sendingdata, address lines for sending addresses, and control lines for sendinginterrupts and for operating the system bus. A nonlimiting example ofsuch a system bus 38 is or includes a PCI (Peripheral ComponentInterconnect) bus.

The computer 24 further includes a memory 40, which may includecomputer-readable storage media in the form of volatile and/ornonvolatile memory such as read only memory (ROM), EEPROM, and RAM. Abasic input/output system (BIOS), containing basic routines as will beappreciated by those ordinary skilled in the art, may be stored in ROM.RAM may contain, for instance, data, data tables, and/or modules (e.g.,program modules) that are immediately accessible to and/or presentlybeing operated on by the processor 32.

The computer 24 may also include, receive, or interface (e.g., via amedia interface 42) with computer-readable storage media 44. Computerreadable storage media 44 include volatile and nonvolatile, removableand non-removable media implemented in any non-transitory (i.e.,tangible or physical) method or technology for storage of information,but such computer readable storage media do not merely include signals.Nonlimiting examples include RAM, ROM, EEPROM, flash memory or othermemory technology, disk drives such as a hard disk drive (HDD), magneticdisk drives, magnetooptical disk drives, optical disk drives such asCD-ROM, BD-ROM, and digital versatile disks (DVD).

Other example computer storage media 44 includes, but are not limitedto, magnetic tape cassettes, flash memory cards, digital versatiledisks, digital video tape, solid state RAM, solid state ROM, etc. Suchdrives and their associated computer storage media can provide storageof computer readable instructions, data structures, program modules andother data for the computer 24.

The computer 24 may include one or more input devices (not shown) forentering commands and information into the computer. Example inputdevices include keyboards, buttons, pointing devices, trackball, touchpads, touch screens, dials, microphones, joysticks, game pads, satellitedishes, scanners, and others. These and other input devices can beconnected to the processor 32 through a suitable user input/outputinterface 46 coupled to the system bus 38, or by other interface and busstructures, such as a parallel port, game port or a universal serial bus(USB). The computer 24 may connect to the network 26 through thecommunication interface 28, such as but not limited to a networkinterface or adapter.

It will be understood that any or all of the apparatuses, systems,methods and processes described herein may be embodied in the form ofcomputer executable instructions (i.e., program code) stored on acomputer-readable storage medium (e.g., a non-transitorycomputer-readable storage medium), which instructions, when executed bya processor such as the processor 32, cause the processor to performand/or implement the systems, methods and processes described herein.Specifically, any of the steps, operations or functions described hereinmay be implemented in the form of such computer executable instructions,executing on the processor of a computer, or apparatus or systemincluding such a computer. Reference herein to any method is intended toalso be a reference (to the extent practicable) to processesincorporating such methods or any portion thereof, to apparatuses orsystems implementing such methods or any portion thereof, or to computerexecutable instructions stored on a computer-readable storage mediumwhich instructions, when executed by the processor, cause the processorto perform and/or implement such methods or any portion thereof.

The example system 20 preferably, but not necessarily, includes adatabase 50. The database 50, if provided, may be in communication withthe computer 24 or incorporated within the computer. The database 50includes memory such as any of the memory 40 mentioned above, and caninclude a processor for executing database software embodied in computerexecutable instructions. The database software can be made available tothe processor of the database 50 in similar or different manner to othercomputer executable instructions herein.

The memory 40 preferably includes (e.g., stores) operating systemsoftware 52, data, e.g., application data 54, that can be processed bythe processor, and application software 56, e.g., one or more modulescontaining computer-executable instructions for causing the processor 32to process the data according to example methods herein. Data stored inthe memory 40 or a portion thereof can additionally or alternatively bestored in the database 50, or stored in the database and transferred(e.g., loaded) into the memory.

FIG. 2 shows an example architecture 60, e.g., global architecture, forcarrying out various inventive methods. The architecture 60 can beembodied by one or more modules embodied in the processor 32 (or anothersuitable processor) and the memory 40 (and/or another suitable memory)as configured by executable instructions, such as but not limited to theapplication software 56 in combination with the application data 54.

The example architecture 60 includes a back-end 62 including a datastorage such as but not limited to a POI knowledge base (KB) 64, a tipontology database 66, a tip identifier module 68, an informationretrieval module 70, and a map service module 72. The example POI KB 64,which may be embodied in database 50, application data 54, or in anyother suitable storage, includes various types of POI information storedtherein. Nonlimiting examples of POI information include standard POIinformation, such as but not limited to POI name, geolocation, address,phone number, website URL or other locator information, POI category(and/or subcategories), images. Further, the POI KB 64 preferablyincludes information such as global and/or aspect-based user ratings andfree-text reviews, crowdsourced tips and Q/A. Additionally, according toexample embodiments, the POI KB 64 (and/or other data storage) includesstored tips that are automatically extracted from the POI free-textreviews.

The example tip ontology 66, which may be embodied in database 50,application data 54, or in any other suitable storage, and may be acomponent of or in addition to the POI KB 64, includes an ontology oftip semantic categories or classes. Semantic categories as used hereinrefer to categories or classes for POI tips that are based, preferablyentirely, on the semantics of the tips. The tip ontology 66 can beembodied in a simple flat set of semantic classes, a hierarchical setwith a set of main (top) classes and subclasses, or any combination.More than one ontology (set) can be provided.

FIG. 3 shows an example of a flat tip ontology 74, with various tipontology classes (tip classes) 76 and tip examples 78 for each tipclass. The example tip ontology classes 76 shown in FIG. 3 include:Accessibility and Physical Conditions; For whom and with whom to visit;Cost and Mode of Payment; Visit Period/Time; What to Wear/How to Dress;What to Bring; Onsite Facilities/Services; and Onsite Activities. Itwill be appreciated that the tip classes 76 and tip examples 78 shown inin FIG. 3 are merely illustrative, and that many other tip classes maybe used to classify tips and other tip examples are possible.

The example tip identifier module 68 includes a processing component,such as a natural language processing (NLP) component or components,which implements a tip identifier model that may be embodied in, forinstance, an NLP model. The tip identifier module 68 employs the tipidentifier model to automatically process POI free-text reviews toidentify tip occurrences in the POI free-text reviews, and classifiesthe identified tips according to one or more of the determined tipontology classes, such as those stored in the tip ontology 66. Thisextracts such tips from the POI free-text reviews for later retrieval.The extracted tip occurrences are then preferably associated with (e.g.,indexed by), at least a unique identifier for the POI (POI UID), aunique identifier (UID) identifying the source of a POI free-text reviewfrom which the tip was extracted, and by tip labels (categories), andstored in the POI KB 64.

As an example of such automatic processing, POI free-text reviews may beprocessed by the tip identifier module 68 as they are added to the POIKB 64 for retrieval during a POI search. Alternatively or additionally,POI free-text reviews can be obtained separately and added to the POI KB64. “As” can refer to being either before, during, or after the POIfree-text review is added to the POI KB 64, but preferably takes placeprior to being prompted or requested to do so by a user. This processingcan occur as individual POI free-text reviews are added, or can occur inbatches.

The example information retrieval module 70 is configured to retrieveinformation from the POI KB 64 relating to at least one POI,particularly a matching POI in response to a received query. Thisretrieved information includes at least a set of the identified tipoccurrences (tips) relating to the at least one POI and their respectiveassociated semantic categories. To retrieve this information, theinformation retrieval module 70 searches (preferably efficiently) all ora portion of the POI KB 64 data, including the automatically extractedtip occurrences, and in some example embodiments the POI free-textreviews, which preferably are associated with particular POIs, such asvia a data structure. Example methods for searching the POI KB 64include but are not limited to standard information retrieval techniquesas used in search engines (e.g., Lucene or ElasticSearch), and othersthat will be appreciated by those of ordinary skill in the art.

The map service module 72 is configured to generate a user interface,such as a map interface, providing a visualization of one or more POIsretrieved during a search of the POI KB 64 by the information retrievalmodule 70 in response to a user query for POIs. The map service module72 preferably generates a map including locations of the retrieved POIsfor selection by the user.

A front-end 80 of the global architecture 60 provides (e.g., generatesand transmits) a user interface for interacting with users. Thefront-end 80 connects to the back-end 62, e.g., via an interface such asbut not limited to a representational state transfer applicationprogramming interface (REST API) 82.

A POI registration interface module 84 generates a front-end userinterface (UI) for allowing a user to register new POIs. A POI reviewentry interface module 86 generates a front-end UI for allowing a userto enter new reviews, or revise or delete existing reviews, for existingPOIs.

To receive a query, for instance a query for a POI from a user computingdevice 22 via the communication interface 38, the front-end 80 includesa POI query interface module 88 that generates and transmits a front-endUI. An example front-end UI for receiving a query includes aninteractive POI search form (e.g., a standard search form or othersearch form) for querying POIs, as will be appreciated by those ofordinary skill in the art.

To present results in response to the received query, the front-end 80further preferably includes a matching POI interface module 90 and a POIdetail interface module 92. The matching POI interface module 90generates and transmit an interactive user interface interactivelydisplaying at least a portion of a retrieved set of matching POIs. Thesedisplayed matching POIs can, for instance, be displayed on a map asprovided by the map service module 72. Alternatively or additionally,the matching POIs can be presented in list form, or in other formats.Preferably, the displayed matching POIs in the user interface areindividually selectable (via any suitable method) to provide one or morePOI detail interfaces generated and transmitted by the POI detailinterface module 92, examples of which are provided below.

FIG. 4 shows an example method for populating a data storage, such asthe POI KB 64, with tips extracted from POI free-text reviews. Steps inan example method can be performed, for instance, by the tip identifiermodule 68 operating in communication with the POI KB and the tipontology 66. The tip identifier module 68 generally employs a trainedtip identifier model, e.g., a trained NLP model, to identify occurrencesof tips in POI free-text reviews, labels the identified occurrences oftips with one or more semantic categories (e.g., from tip ontology 66),and stores the extracted tips and their association with the one or moresemantic categories in the data storage.

In an example method, the identification and labeling of tip targets bythe tip identifier module 68 is presented as a sequence labelingproblem. As such, the identification and labeling can be implementedusing sequence labeling techniques, such as those disclosed inhttps://arxiv.org/abs/1603.01354. However, it is contemplated that othermethods can be used.

In particular example methods, the tip identifier model is implementedusing supervised learning and a neural multi-label classifier.Alternatively, the tip identifier model can be implemented using arule-based method.

Referring to FIG. 4 , the tip ontology can be defined and stored (step100). The semantic categories populating the example tip ontology 66 canbe defined either manually, such as by receiving semantic categories asone or more inputs from any suitable source, and/or can be definedautomatically or semi-automatically, such as but not limited to by usingknown techniques for mining and clustering topics or concepts from anysuitable source such as but not limited to POI reviews. Example methodsfor defining an ontology are disclosed inhttps://arxiv.org/abs/1903.04360. Other example methods for performingthese techniques will be appreciated by those of ordinary skill in theart. The tip ontology 66 may be updated or supplemented similarly. Thedefined tip ontolog(ies) 66 can be stored in any suitable data storage.

Using the defined classes of the tip ontology, training data for the tipidentifier model is generated (step 102), e.g., created, implementedusing hand-crafted rules, implemented using unsupervised learningtechniques, etc. In an example method, occurrences of tips in a set ofrandomly sampled reviews are annotated, e.g., manually annotated, tocreate the training data. In a particular method, spans of text stringsor tokens, e.g., words, are annotated (identified, with the resultpreferably being stored at least temporarily) denoting the core elementof the tip (that is, semantically, the relevant word(s) conveying thecore meaning(s) of the particular tip), as opposed to the entiresentence where the tip occurs. These annotated spans of words arereferred to herein as “tip targets.” Manual annotation can be providedby, for instance, receiving data input by any suitable method and fromany source indicating identified tip targets and associated classes ofthe tip ontology.

Referring again to the tip ontology shown in FIG. 3 , the associated tipexample 78 for each example tip ontology class (tip class) 76 includesone or more spans of words (emphasized using a bolt font) denoting thecore element of the tip, which spans of words are identified as exampletip targets. For instance, for the tip class Accessibility and PhysicalConditions, the tip example labeled with that tip class is “Part of thevisit is possible only by a STAIRCASE of about a hundred steps.” The tiptarget “staircase” (emphasized in FIG. 3 ) is annotated within the tipexample, as it directly conveys particular practical and factualinformation under the semantic category Accessibility and PhysicalConditions. Similarly, for the tip class “Cost and Mode of Payment,” theexample tip example “Visit costs 12 EUROS. Book online for a 15%DISCOUNT” is provided, with the spans “12 EUROS” and “15% DISCOUNT”annotated as tip targets, as each convey particular practical andfactual information under the semantic category “Cost and Mode ofPayment.” Strings of text (e.g., words, phrases, sentences, etc.) in POIfree-text reviews may include multiple tip targets labeled withdifferent semantic categories. Further, the same string of text, oroverlapping strings of text, can be labeled with multiple semanticcategories.

The created training dataset thus preferably includes multiple POIfree-text reviews each associated with zero, one, or more tipoccurrences and their manually associated (e.g., annotated) semanticcategory labels. Reviews that do not contain any tip occurrence (andhence, no annotated spans) preferably also form part of the createdtraining dataset. The latter reviews are considered as negativeexamples, so that the tip identifier model can learn to avoididentifying tip occurrences in reviews or groups of tokens (e.g.,sentences) that do not contain tip occurrences.

Using the created training dataset, the tip identifier model, e.g., aneural model, is trained to identify tip targets and their correspondingsemantic category labels from POI free-text reviews (step 104). Then,the trained tip identifier module receives new POI free-text reviews(step 106), identifies occurrences of tips (tip occurrences) in thereceived POI free-text reviews (step 108), and labels the identified tipoccurrences with selected tip ontology class labels to associate theidentified tip occurrences with the semantic categories (step 110). Thelabeled tip occurrences are then stored in the POI KB (step 112).

FIG. 5 shows steps in an example method for employing a tip identifiermodel to identify and label tip targets. The POI free-text reviews canbe received from any source (step 120). For training the tip identifiermodel, for instance, the POI free-text reviews may be received from thecreated training dataset. For populating the POI KB (or furtherpopulating the POI KB), the POI free-text reviews input into the(trained) tip identifier model may be received from sources includingbut not limited to POI free-text reviews that may be used in a POI KBfor presenting to a user in other contexts or interfaces. For example,free-text reviews are typically (but not necessarily) user-generated;that is, generated by users of POI search systems that enter reviews andratings for the POIs that they have visited. Such free-text reviews arestored as texts in the POI KB.

The received POI free-text review texts may be preprocessed with atokenizer with or without initial processing with a sentence segmenter(step 122). Nonlimiting example tokenizers and sentence segmentersinclude spaCy (https://spacy.io/api/tokenizer) and TokenizeProcessor(https://stanfordnIp.github.io/stanfordnIp/tokenize.html), or thosedisclosed inhttps://cl.lingfil.uu.se/˜marie/undervising/textanalys16/TokeninsationSegmentation.pdf.The tokenizer and (if used) sentence segmenter provide sequences oftokens (words, punctuation symbols, etc.), represented with pretrainedword embeddings. Context vectors are generated, e.g., for each token(e.g., word) (step 124).

For example, a multi-label word classifier can be implemented in the tipidentifier model. In an example multi-label word classifier, the inputwords of each sentence are fed into a recurrent neural network (RNN)such as but not limited to a bidirectional long short-term memoryarchitecture (BiLSTM) augmented with a self-attention mechanism. Anexample BiLSTM architecture is disclosed inhttps://arxiv.org/abs/1603.01354, which is incorporated herein byreference.

The resulting context vector of each word is then connected to afeedforward network that outputs a generated prediction score for eachof the predefined semantic categories (step 126). For example, thefeedforward network can output a vector of probabilities for each of thepredefined tip ontology classes.

The prediction score, e.g., vector of probabilities, can then be used toprovide a set of extracted tip occurrences and their associated semanticcategory labels (step 128). For example, for each word, the vector ofprobabilities can be compared to one or more thresholds to determinewhether the word should be identified as a tip occurrence that isassociated with one or more of the semantic categories represented bythe vector of probabilities. If the determined probability exceeds (ormeets or exceeds) the threshold for one or more particular semanticcategories, the corresponding word is added to a set of extracted(identified) tip occurrences that is associated (labeled) with suchsemantic categor(ies). If the tip identifier model is merely beingtrained, the extracted tip occurrences and associated labels can becompared to the annotated tip occurrences and labels, and the result ofthis comparison can be used to update the tip identifier model (step130). Nonlimiting example training methods are disclosed in IanGoodfellow et al., 6.5 Back-Propagation and Other DifferentiationAlgorithms, Deep Learning, MIT Press (2016), pp. 200-220, which isincorporated herein by reference. Otherwise, the set of labeled tipoccurrences is then stored in the data storage (step 112) (FIG. 4 ).

Preferably, the example process of identifying and classifying tips inPOI free-text review runs only once for each review, as soon as it isavailable, and returns a set of labeled tip occurrences. The tipidentifier module 68 then stores the labeled tip occurrences for thereview in the data storage, such as the POI KB, to update the datastorage.

A tip occurrence preferably is stored in the form of a predefinedstructure, an example of which can include one or more of the followingfields, in any combination: the character-based start/end offsets of thetip target in the text of the POI free-text review; the character-basedstart/end offsets of the sentence where it occurs; the set of predictedtip semantic category labels; a unique identifier (ID) of the POI, and aunique ID of the POI free-text review. All extracted tip occurrences arepreferably thus indexed on at least the POI unique ID and the tip classlabels. They are preferably stored in the POI KB 64 along with theassociated POI free-text reviews themselves and with other POIinformation such as the standard POI information and/or other exampleinformation disclosed herein.

Using the above methods, for example, tips are automatically selectedfrom free-text reviews and grouped into semantic categories. In responseto a query, these tips can then be retrieved and presented via a userinterface, preferably along with other POI-related information, toprovide useful information to the user relating to the POI.

FIG. 6 shows steps in an example method for providing information to auser relating to a POI, including retrieving and presenting categorizedPOI tips, in response to a user's query, e.g., a query for a POImatching certain input criteria such as during a POI search andexploration (though such tips may be generated in response to otherqueries). The query is received (step 140), e.g., via the communicationnetwork 26 via a search interface, such as but not limited to those usedfor conventional POI searches (e.g., standard POI search forms), whichmay be provided by the POI query interface module 88.

The received query can be processed, e.g., parsed, by the informationretrieval module 70 using methods known to those of ordinary skill inthe art for information searching. The information retrieval module 70searches the data storage, such as the POI KB 64, and retrieves matchingPOIs based on the processed query (step 142). It will be appreciatedthat “matching” need not require an exact match with any particular partof the received or processed query, only that the resulting POIs relateto the received or processed query to provide a potentially relevantresult.

In an example method, the matching POI interface module 90 generatesmatching POIs, e.g., a list of matching POIs (step 144), and transmitsto the user an initial matching POI user interface (initial interface)presenting, e.g., displaying, one or more of the matching POIs (step146). Preferably, each of the displayed POIs in the matching POI userinterface is selectable by the user, either individually or as one ormore groups. The format for the matching POI user interface can vary,and a specific format is not required. For instance, the matching POIuser interface can be a formatted interactive list, displayed on one ormore interactive panels.

Alternatively or additionally, the map service module 72 can retrieve amap (not shown) corresponding to one or more of the list of matchingPOIs, and the matching POI interface module 90 can generate, as part ofthe matching POI user interface, a user interface incorporating theretrieved map, preferably displaying the matching POIs thereon (such aswith appropriate icons, pins, etc.). A subset of the matching POIs maybe viewable at the same time, which subset may be updated as the userinteracts with the map interface (e.g., scrolling, zooming, etc.) Thematching POIs may be selectable from the map and/or from other formattedportions of the matching POI user interface.

The initial interface may include the displayed list of matching POIs(or a selectable portion thereof), preferably along with one or moreitems of basic information (e.g., POI name, rating, representativeimage, etc.). This displayed basic information preferably aids a user ingenerally reviewing the list of matching POIs and making a selectionamong them to request more detailed information. The initial interfaceis preferably easily and intuitively navigable by the user to review thedisplayed matching POIs and make a POI selection therefrom.

The matching POI interface module 90 receives a user selection of one ormore POIs from the displayed matching POI list (step 148), such as bythe user selecting (e.g., clicking) a corresponding icon, widget, link,etc. on the presented initial interface. In response to the received POIselection, the information retrieval module 70 searches for andretrieves more detailed information from the POI KB that is associatedwith the selected POI(s). It is also contemplated that this search formore detailed information can occur at least in part during the initialsearch for matching POIs, and stored in memory for later retrieval bythe information retrieval module 70 in response to the received POIselection.

The POI detail interface module 92 then generates an additional userinterface (a POI detail interface) presenting, e.g., displaying, moredetailed POI information about the selected POI(s) using the additionalinformation retrieved from the POI KB (step 150). This POI detailinterface may be displayed on a separate screen (e.g., separate page,tab, or document) from the POI matching user interface, may beconcurrently displayed with the POI matching user interface, may beincorporated within the POI matching user interface, may be superimposedon the POI matching user interface, or may otherwise be presented. Anysuitable transition (or none at all) between the POI matching userinterface and the POI detail interface may be provided.

Preferably, the POI detail interface includes at least a first userinterface portion, e.g., a first panel (not shown), including displayedstandard information for one or more of the selected POIs. The firstpanel can include, for example, a formatted list of standard POIinformation, such as but not limited to a POI's address, phone number,website URL (if any), geolocation, map visualization, representativeimage, ratings, etc. The first panel (or other user interface) may alsoinclude one or more displayed sections to view and/or access userreviews, and crowdsource Q/A and tips, if any. The first panel (or otheruser interface) may be expandable or collapsible to display or hideparticular information.

Also, to provide an example map visualization, the map service module 72can retrieve a map corresponding to one or more of the selected POIs (ifone has not previously been retrieved), and the POI detail interfacemodule 92 can generate, as part of the POI detail interface, a userinterface incorporating the retrieved map, preferably displaying theselected POIs thereon (such as with appropriate icons, pins, etc.). Asubset of the selected POIs may be viewable at the same time, whichsubset may be updated as the user interacts with the map interface(e.g., scrolling, zooming, etc.). The displayed selected POIs may beselectable from the map and/or from other formatted portions of the POIdetail interface. Preferably, additionally selected POIs from thematching POIs can be added to the generated map. Previously selectedPOIs may also be removable (de-selected) from the map in someembodiments.

Additionally, upon receiving a request for categorized tips (step 152),for instance in response to receiving the POI selection or in responseto receiving a further selection relating to a particular POI in thefirst user interface portion, the information retrieval module 70searches for and retrieves previously extracted tips and associatedcategories from the POI KB that are associated with the selected POI(s).It is also contemplated that this search for extracted tips and semanticcategories can occur at least in part during the initial search formatching POIs, and stored in memory for later retrieval by theinformation retrieval module 70 in response to the received POIselection or further selection.

The POI detail interface module generates and transmits, as part of thePOI detail interface, a second user interface portion interactivelypresenting the retrieved tips associated with the selection POI(s),sorted by their associated semantic categories (step 154). This seconduser interface portion may be displayed on a separate screen (e.g.,separate page, tab, or document) from the first user interface portion,may be concurrently displayed with the first user interface portion, maybe incorporated within or combined with the first user interfaceportion, may be superimposed on the first user interface portion, or mayotherwise be presented. Any suitable transition (or none at all) betweenthe first user interface portion and the second user interface portionmay be provided.

In some example embodiments, the second user interface portion isembodied in a second panel that is generated and transmitted to theuser, preferably along with the first panel (and may be combined withthe first panel in some embodiments). FIGS. 7-9 show a second panel 180provided as a section bar in three different states according to anexample embodiment. This second panel section bar 180 can also beincorporated in the POI matching interface and/or in the POI detailinterface.

Preferably, the second panel 180 is configured to initially hide fromview the retrieved POI tips and associated semantic categories untilselected by the user. For instance, the second panel 180 can initiallybe collapsed, and then expand at least partially upon selection of anexpandable/collapsible section bar 182, such as a “Tips” section bar, inthe second panel.

The second panel 180 displays the retrieved POI tips as correspondingcollapsible/expandable panels to users. Preferably,collapsible/expandable panels are provided for each retrieved tipcategory associated with the selected POI. Only tip categories that haveinstances in the reviews of the selected POI are preferably displayed ascollapsible/expandable panels, though it is also contemplated to includea null, greyed, or blank tip category to indicate that no information isavailable for a particular category.

For example, in the panel 180 shown in FIG. 7 for the selected POIGrotte de Choranche, semantic tip category panels 184 are displayed fromamong those provided in the tip classes 76 (FIG. 3 ), including:Accessibility and Physical Conditions; For whom and with whom to visit;Cost and Mode of Payment; Visit Period/Time; What to Wear/How to Dress;Onsite Facilities/Services; and Onsite Activities. In this example, notip was previously automatically extracted for the selected POI in thesemantic tip category What to Bring shown in FIG. 3 , and thus acorresponding collapsible/expandable panel is omitted in the examplesecond panel 180 in FIGS. 7-9 .

As shown in FIG. 7 , the semantic tip categories 184 are initiallydisplayed as collapsed section bars. Preferably, a user can thendirectly access a specific displayed category of tips by making a tipcategory selection, e.g., by selecting (such as clicking) thecorresponding panel. In response to receiving the tip category selection(step 156) (FIG. 6 ), the associated tip category panel 184 is expandedto reveal one or more associated tip occurrences (tips) 186 (step 158).If thereafter a new tip category selection is made (step 160), the listof tips can be, but need not be, removed from view (step 162), and a newlist of tips in the newly selected category can be displayed (step 164).

For example, FIG. 8 shows the user interface portion (second panel) 180of FIG. 7 , in a state where a tip category panel 188 corresponding totip category “Accessibility and Physical Conditions” is selected fromamong the tip category panels 184 and expanded. FIG. 9 , on the otherhand, shows the user interface portion (panel) 180 of FIG. 7 , in astate where a tip category panel 190 corresponding to tip category “Costand Mode of Payment” selected and expanded, and with the “Accessibilityand Physical Conditions” 186 collapsed. Panels 184 corresponding todisplayed semantic tip categories can be configured to automaticallycollapse upon selection of a different tip category panel, remainexpanded unless collapsed by a user, collapse unless pinned by a user,or collapse or expand in other ways. Preferably, as mentioned thecomplete second panel 180 can likewise be collapsed (e.g., folded), suchas to a single section bar (e.g., the “Tips” section bar 182) to free UIspace for other information, such as other POI information.

Referring again to FIG. 8 , the (expanded) section panel for theselected semantic category “Accessibility and Physical Conditions”provides a tip occurrence list view, e.g., a window or cell 192,revealing categorized tips 186 automatically extracted from thefree-text reviews of the selected POI associated with (and preferablyindexed in the POI KB 64 with) the semantic category Accessibility andPhysical Conditions (or associated semantic category in the POI KB, ifthe displayed category names vary from the stored category names).

As further shown in FIG. 8 , upon selection of the semantic category“Accessibility and Physical Conditions,” the associated tip occurrences186 preferably are at least initially displayed in the tip occurrencelist view 192 in the form of text snippets. A text snippet as usedherein is a displayed portion (e.g., string) of text, which may have atoken (e.g., word) length or count that is equal to or smaller than thecomplete POI free-text review from which the tip is extracted, and inwhich at least one tip target 198 (tip core element) is emphasized onthe display. An example emphasis for the tip target(s) 198 includesknown text emphasis methods, such as bolding, highlighting, alternatecolor, blinking, enlarging, etc.

The length and content of text snippets may be selected and displayedbased on various criteria. In some example embodiments, the textsnippets may be the complete sentences in which the tip target 198occurs. As another example, the text snippet may be a text window thatincludes the tip target 198, and a number (n, where n can be acustomizable parameter set by the user or in the system 20) of words orother tokens (or meaningful words or tokens) before and after the tiptarget. Ellipses or other indicators may be provided to indicate omittedtext.

A tip occurrence list view 192 can, but need not, include a maximum size(absolute or proportional) for text display, which size may becustomizable using methods that will be apparent to those of ordinaryskill in the art. If a maximum size is thus provided, the tip occurrencelist view 192 preferably becomes scrollable when the maximum size isexceeded.

The example list of tip occurrences displayed in a particular categoryin the tip occurrence list view 192 may be automatically sorted, orselectively sorted, by one or more criteria. As a nonlimiting example,if a timestamp is generated during the source POI free-text review andstored with this review, the tip occurrences can be sorted by thetimestamp after retrieval from the POI KB 64. For instance, the newest(or oldest) tip occurrences may be displayed first.

Preferably, the example user interface, such as the POI matchinginterface, the first user interface portion of the POI detail interface,or the second user interface portion of the POI detail interface,includes one or more user feedback or control features, including butnot limited to icons, widgets, or other controls. Any suitable interfacethat can allow the user to access and/or select/deselect the particulartype of information they are interested in can be provided. Suchcontrols can be provided, as a nonlimiting example, as directlyaccessible controls and/or via a menu. Such controls or menus can beaccessed, for instance, by selecting suitable provided widgets or iconson (or near) one or more tip category panels 184 and/or section bars 182(e.g., at a corner of the tip category or section bar, or elsewhere)and/or on one or more displayed tips 186 in the tip occurrence listview.

Such controls can allow a user, for instance, to selectively order orre-order the displayed tip occurrence according to the timestampinformation, as disclosed above. Alternatively or additionally, controlsmay be provided to re-order the list of tip occurrences, for instance,by a relevance score. An example relevance score is the prediction scorewith which the tip identifier has predicted a tip occurrence and itsassociation with the selected semantic category. Other criteria forselectively ordering the displayed tip occurrences are possible.

In example embodiments, receiving a selection (e.g., click) of adisplayed tip occurrence 186 (step 166) (FIG. 6 ) expands the selectedtext snippet to show the full POI review text, or at least an expandedportion (for instance, up to a predetermined length) of the POI reviewtext (step 168). Additional information relating to the selected tipoccurrences 186 can also be provided, such as but not limited totimestamp and/or author information. Preferably, the displayed expandedPOI review text can then be selected again to cause a return to the textsnippet view (e.g., as shown in FIGS. 8-9 ). Other controls, such as“collapse all” can also be provided.

In example embodiments, in the tip occurrence list view 192 of the(expanded) second user interface portion shown in FIGS. 8-9 , icons,widgets, or other interactive controls 196 may be provided with one ormore displayed text snippets or expanded tips to receive user feedbackregarding the displayed tips (tip feedback). For instance, interactivecontrols may be provided with which users can “upvote” or “downvote” orotherwise rate the tip occurrence in which the tip snippet resides,e.g., based on its accuracy and/or importance. In the example controls196 shown in FIGS. 8-9 , up arrows and down arrows are shown, but otherdisplayed icons or controls, such as thumbs-up/down, check/uncheck,happy/sad face, “X” or “0”, “Like” or “Dislike,” a sliding rating scale,a form field for entering the feedback or any other indicators to conveyupvoting or downvoting or otherwise rating or providing tip feedbackregarding the user's view of the relevance of the particular displayedtip. “Relevance” can generally also refer to perceived quality,usefulness, user appreciation, or any other criterion for userassessment of the particular tip.

FIG. 10 shows an example method for employing tip feedback. The tipfeedback is provided by a user, such as via any of the exampleinteractive controls disclosed above (or others) (step 200). Thisfeedback is received by the front end 80, for instance by the POI detailinterface module 92.

A relevance score is then determined, e.g., calculated, based on anaggregated users' relevance score (step 202). The relevance score can becomputed, for instance, by the tip identifier module 68 or otherback-end 62 or front-end 80 module, from multiple users' collective oraggregate feedback received by the front-end (as a nonlimiting example,from received upvoting or downvoting actions, or by using other userfeedback). Any suitable method can be used to calculate this relevancescore from the received feedback. As a nonlimiting example, upvoting canincrement an appreciation score (an example or component of a relevancescore) by a particular amount, while downvoting can decrement theappreciation score by the same or a different amount. As anotherexample, feedback rating scores received either directly (e.g., via aform field or sliding scale) or indirectly (e.g., by assigning downvotesto a zero and upvotes to 10 or some other number) can be averaged orotherwise combined to provide a relevance score.

Individual feedback (e.g., individual votes or scores) can be, but neednot be, weighted when determining relevance scores. Feedback can also beselectively considered or omitted from consideration in thedetermination. For instance, older feedback may be given less weightthan more recent feedback. As another example, to prevent excessive,inaccurate, or malignant upvoting/downvoting of tips, a limitation perPOI tip may be set, e.g., based on a user's unique ID (e.g., an accountID if logged in, and/or a user's IP address if not). It is possible thatmore than one relevance score could be determined for differentapplications.

The determined relevance score(s) can be used in various ways accordingto example methods. For instance, individual tips 186 displayed in thetip occurrence list view 192 for a particular semantic category can besorted in decreasing order of their determined relevance scoreautomatically. Alternatively, each of the tip category section bars 184may include one or more controls to selectively re-order the list of tipoccurrences based on the determined relevance score. It is alsocontemplated that tip occurrences having a relevance score below a setthreshold may be omitted from the display (e.g., in a reduced view, oreven in an expanded view).

The relevance score can also in some example embodiments can be combinedwith other determined relevance scores, such as that based on aprediction score as described above, with any suitable weights assignedto each score, to determine a combined relevance score. This combinedrelevance score can alternatively or additionally be used toautomatically or selectively sort displayed tip occurrences 186 asdisclosed herein.

Preferably, on a regular or semi-regular basis, POI tips generated(e.g., identified) by the tip identifier model that receive a determinedrelevance score (e.g., based on user feedback as described above)greater than a threshold (or based on other criteria relating to thedetermined relevance score), can be selected (step 204) and incorporatedas positive examples to an example augmented training dataset forretraining the example tip identifier model. The example tip identifiermodel may then be regularly retrained on the so-augmented trainingdataset (step 208).

For instance, after a predetermined amount of new POI tips (that is,those not yet in the (preferably continuously evolving) trainingdataset) have been received, a predetermined number of the identifiedPOI tips generated within the new set of POI tips having the highestdetermined relevance scores can be incorporated into the augmentedtraining set. If an unusual number of identified POI tips receive lowrelevance scores, a relevance score cutoff may be used to avoidincorporation of low-scoring identified POI tips. As another example,after a certain number of identified POI tips receive a relevance scoreexceeding a certain threshold, these identified POI tips areincorporated into the augmented training set. The tip identifier modelcan then be retrained using the augmented training set, e.g., using themethods disclosed herein.

Methods and systems are disclosed herein for providing information to auser relating to a point-of-interest (POI). An example method comprises:receiving a query via a communication network; retrieving, using aprocessor, information from a data storage relating to at least one POIbased on the received query, wherein the retrieved information comprisesa set of retrieved POI tips relating to the at least one POI, each ofthe set of retrieved POI tips being previously extracted from POIfree-text reviews and respectively associated in the data storage withone or more semantic categories based on a predefined tip ontology;generating, using the processor, a user interface interactivelydisplaying the retrieved POI tips and the semantic categories associatedwith each of the retrieved POI tips, wherein the displayed POI tips aresorted by their associated displayed semantic categories; andtransmitting the generated user interface to a computing device of theuser via the communication network for display on the computing device.

In particular embodiments, and in combination with any of the abovefeatures, each of the displayed semantic categories in the userinterface is selectable to display one or more of the retrieved POI tipsassociated with the respective displayed semantic category. Inparticular embodiments, and in combination with any of the abovefeatures, each of the displayed semantic categories comprises anexpandable/collapsible widget. In particular embodiments, and incombination with any of the above features, each of the displayed POItips comprises a text snippet including a tip target. In particularembodiments, and in combination with any of the above features, the textsnippet further comprises either a source sentence from the POIfree-text review from which the displayed POI tip was extracted or apredefined number of words surrounding the tip target from the POIfree-text review from which the displayed POI tip was extracted; whereinthe text snippet is selectable to display an expanded portion of the POIfree-text review from which the displayed POI tip was extracted; andwherein an appearance of the displayed tip target is visibly distinctfrom other portions of the displayed text snippet. In particularembodiments, and in combination with any of the above features, the textsnippet is further selectable to display at least one of a date orauthor of the POI free-text review from which the displayed POI tip wasextracted.

In particular embodiments, and in combination with any of the abovefeatures, the user interface comprises: a first user interface portiondisplaying the retrieved information other than the retrieved POI tipsand the semantic categories associated with each of the retrieved POItips; and a second user interface portion interactively displaying theretrieved POI tips and the semantic categories associated with each ofthe retrieved POI tips.

In particular embodiments, and in combination with any of the abovefeatures, the retrieved information displayed by the first userinterface portion comprises standard POI information. In particularembodiments, and in combination with any of the above features, whereinthe first user interface portion and the second user interface portionare concurrently displayed in the user interface; and wherein, in thesecond user interface portion, the retrieved POI tips and the semanticcategories associated with each of the retrieved POI tips are initiallyhidden until the second user interface portion is selected.

In particular embodiments, and in combination with any of the abovefeatures, the second user interface portion comprises anexpandable/collapsible widget. In particular embodiments, and incombination with any of the above features, the displayed tips withineach of the associated displayed semantic In particular embodiments, andin combination with any of the above features, the user interfacefurther comprises an interactive widget for receiving feedback for oneor more of the displayed tips within each of the associated displayedsemantic categories. In particular embodiments, and in combination withany of the above features, the tips within a category are furthersortable by a relevance score that is calculated at least partiallybased on the received feedback. In particular embodiments, and incombination with any of the above features, the data storage comprises aPOI knowledge base (KB).

In particular embodiments, and in combination with any of the abovefeatures, the method further comprises: retrieving, using the processor,a plurality of POI free-text reviews; identifying tip occurrences ineach of the retrieved plurality of POI free-text reviews using a naturallanguage processing model; associating the identified tip occurrenceswith the at least one stored semantic category using the naturallanguage processing model; and storing the identified tip occurrencesand their association with the at least one stored semantic category toprovide the extracted POI tips.

In particular embodiments, and in combination with any of the abovefeatures, the natural language processing model comprises a tipidentifier model. In particular embodiments, and in combination with anyof the above features, the tip identifier model comprises a neuralnetwork.

In particular embodiments, and in combination with any of the abovefeatures, the method further comprises, for each of the retrievedplurality of POI free-text reviews: preprocessing the POI free-textreview to generate one or more input words; and feeding each of thegenerated one or more input words into the tip identifier model. Inparticular embodiments, and in combination with any of the abovefeatures, said associating comprises, for each of the one or more inputwords fed into the tip identifier model: generating, by the tipidentifier model, a prediction score for each of the at least one storedsemantic categories; and associating the input word with one or more ofthe at least one stored semantic categories based on the predictionscore. In particular embodiments, and in combination with any of theabove features, the displayed tips within each of the associateddisplayed semantic categories are sortable by one or more of a reviewdate/time or a relevance score based on the prediction score.

In particular embodiments, and in combination with any of the abovefeatures, the method further comprises: defining and storing the tipontology; creating training data from POI free-text reviews; trainingthe tip identifier model using the created training data; and storingthe trained tip identifier model.

In particular embodiments, and in combination with any of the abovefeatures, the method further comprises: receiving feedback for one ormore of the displayed tips; calculating a relevance score for the one ormore of the displayed tips at least partially based on the receivedfeedback; selecting one or more of the identified tip occurrences basedon the calculated relevance score; generating augmented training dataincluding the selected one or more tip occurrences; and retraining thetip identifier model using the generated augmented training data.

In particular embodiments, and in combination with any of the abovefeatures, the feedback is received from an interactive widget in theuser interface for one or more of the displayed tips within each of theassociated displayed semantic categories. In particular embodiments, andin combination with any of the above features, the interactive widgetcomprises interactive upvoting/downvoting buttons.

Other embodiments disclosed herein provide systems for providinginformation to a user relating to a point-of-interest (POI) comprising:a communication interface; a back-end including a processor and amemory, the back-end comprising: a knowledge base; a tip identifiermodule configured to identify tip occurrences in each of a plurality ofPOI free-text reviews using a natural language processing model,associate the identified tip occurrences with at least one semanticcategory based on a predefined tip ontology using the natural languageprocessing model, and store the identified tip occurrences and theirassociation with the at least one semantic category in the knowledgebase; and an information retrieval module configured to retrieveinformation from the knowledge base relating to at least one POI inresponse to a received query, wherein the retrieved informationcomprises a set of the identified tip occurrences relating to the atleast one POI and their respective associated semantic categories. Afront-end including the processor and memory comprises: a POI queryinterface module configured to receive the query via the communicationinterface; and a POI detail interface module configured to generate andtransmit a user interface interactively displaying the retrieved POItips and the semantic categories associated with each of the retrievedPOI tips, wherein the displayed POI tips are sorted by their associateddisplayed semantic categories.

In particular embodiments, and in combination with any of the abovefeatures, the retrieved information from the information retrievalmodule comprises a set of matching POIs corresponding to the receivedquery, the set of matching POIs including the at least one POI; wherein,for each of the at least one POI, the retrieved information from theinformation retrieval module further comprises standard POI informationrelating to the at least one POI.

In particular embodiments, and in combination with any of the abovefeatures, the user interface generated by the POI detail interfacemodule further interactively displays the standard POI information in afirst user interface portion of the user interface. In particularembodiments, and in combination with any of the above features, the userinterface generated by the POI detail interface module displays theretrieved POI tips and the semantic categories associated with each ofthe retrieved POI tips in a second user interface portion of the userinterface in which the retrieved POI tips and the semantic categoriesassociated with each of the retrieved POI tips are initially hiddenuntil the second user interface portion is selected.

In particular embodiments, and in combination with any of the abovefeatures, the front-end further comprises: a matching POI interfacemodule configured to generate and transmit a matching POI user interfaceinteractively displaying at least a portion of the retrieved set ofmatching POIs, the displayed matching POIs being selectable; wherein thePOI detail interface module is configured to generate and transmit theuser interface for the at least one POI in response to the at least onePOI being selected in the matching POI user interface.

In particular embodiments, and in combination with any of the abovefeatures, at least one of the first user interface portion, the seconduser interface portion, or the matching POI interactive user interfaceis collapsible and expandable.

In particular embodiments, and in combination with any of the abovefeatures, the back-end further comprises: a map service moduleconfigured to generate a map interface comprising a visualization of oneor more of the matching POIs in the matching POI user interface.

In particular embodiments, and in combination with any of the abovefeatures, wherein the second user interface portion further comprises,for each of the displayed POI tips, an interactive widget for receivingfeedback for the displayed POI tip. In particular embodiments, and incombination with any of the above features, the tip identifier module isfurther configured to: calculate a relevance score for the one or moreof the displayed tips at least partially based on the received feedback;select one or more of the identified tip occurrences based on thecalculated relevance score; generate augmented training data includingthe selected one or more tip occurrences; and retrain the naturallanguage processing model using the generated augmented training data.

The foregoing description is merely illustrative in nature and is in noway intended to limit the disclosure, its application, or uses. Thebroad teachings of the disclosure may be implemented in a variety offorms. Therefore, while this disclosure includes particular examples,the true scope of the disclosure should not be so limited since othermodifications will become apparent upon a study of the drawings, thespecification, and the following claims. It should be understood thatone or more steps within a method may be executed in different order (orconcurrently) without altering the principles of the present disclosure.Further, although each of the embodiments is described above as havingcertain features, any one or more of those features described withrespect to any embodiment of the disclosure may be implemented in and/orcombined with features of any of the other embodiments, even if thatcombination is not explicitly described. In other words, the describedembodiments are not mutually exclusive, and permutations of one or moreembodiments with one another remain within the scope of this disclosure.

Each module may include one or more interface circuits. In someexamples, the interface circuits may include wired or wirelessinterfaces that are connected to a local area network (LAN), theInternet, a wide area network (WAN), or combinations thereof. Thefunctionality of any given module of the present disclosure may bedistributed among multiple modules that are connected via interfacecircuits. For example, multiple modules may allow load balancing. In afurther example, a server (also known as remote, or cloud) module mayaccomplish some functionality on behalf of a client module. Each modulemay be implemented using code. The term code, as used above, may includesoftware, firmware, and/or microcode, and may refer to programs,routines, functions, classes, data structures, and/or objects.

The term memory circuit is a subset of the term computer-readablemedium. The term computer-readable medium, as used herein, does notencompass transitory electrical or electromagnetic signals propagatingthrough a medium (such as on a carrier wave); the term computer-readablemedium may therefore be considered tangible and non-transitory.Non-limiting examples of a non-transitory, tangible computer-readablemedium are nonvolatile memory circuits (such as a flash memory circuit,an erasable programmable read-only memory circuit, or a mask read-onlymemory circuit), volatile memory circuits (such as a static randomaccess memory circuit or a dynamic random access memory circuit),magnetic storage media (such as an analog or digital magnetic tape or ahard disk drive), and optical storage media (such as a CD, a DVD, or aBlu-ray Disc).

The systems and methods described in this application may be partiallyor fully implemented by a special purpose computer created byconfiguring a general purpose computer to execute one or more particularfunctions embodied in computer programs. The functional blocks,flowchart components, and other elements described above serve assoftware specifications, which may be translated into the computerprograms by the routine work of a skilled technician or programmer.

The computer programs include processor-executable instructions that arestored on at least one non-transitory, tangible computer-readablemedium. The computer programs may also include or rely on stored data.The computer programs may encompass a basic input/output system (BIOS)that interacts with hardware of the special purpose computer, devicedrivers that interact with particular devices of the special purposecomputer, one or more operating systems, user applications, backgroundservices, background applications, etc.

It will be appreciated that variations of the above-disclosedembodiments and other features and functions, or alternatives thereof,may be desirably combined into many other different systems orapplications. Also, various presently unforeseen or unanticipatedalternatives, modifications, variations, or improvements therein may besubsequently made by those skilled in the art which are also intended tobe encompassed by the description above and the following claims.

What is claimed is:
 1. A method for providing information to a userrelating to a point-of-interest (POI), the method comprising: receivinga query via a communication network retrieving, using a processor,information from a data storage relating to at least one POI based onthe received query, wherein the retrieved information comprises a set ofretrieved POI tips relating to factual information of the at least onePOI, each of the set of retrieved POI tips being previously extractedusing a natural language processing model from POI free-text reviewsassociated with one or more input words, which are reviews of POIsprovided in a free-text structure, and respectively associated in thedata storage with one or more semantic categories based on a predefinedtip ontology, where (i) each extracted POI tip is associated with anoccurrence of an identified tip in the corresponding POI free-textreview using the natural language processing model and the identifiedtip's association with the at least one semantic category; and (ii) eachsemantic category of the predefined tip ontology organizes POI tips bytype and is associated with a prediction score generated by a tipidentifier model based on the one or more input words of thecorresponding POI free-text review; generating, using the processor, auser interface interactively displaying the retrieved POI tips and thesemantic categories associated with each of the retrieved POI tips,wherein the displayed POI tips are sorted by their associated displayedsemantic categories; and transmitting the generated user interface to acomputing device of the user via the communication network for displayon the computing device.
 2. The method of claim 1, wherein each of thedisplayed semantic categories in the user interface is selectable todisplay one or more of the retrieved POI tips associated with therespective displayed semantic category.
 3. The method of claim 1,wherein each of the displayed POI tips comprises a text snippetincluding a tip target; wherein the text snippet further compriseseither a source sentence from the POI free-text review from which thedisplayed POI tip was extracted or a predefined number of wordssurrounding the tip target from the POI free-text review from which thedisplayed POI tip was extracted; wherein the text snippet is selectableto display an expanded portion of the POI free-text review from whichthe displayed POI tip was extracted; and wherein an appearance of thedisplayed tip target is visibly distinct from other portions of thedisplayed text snippet.
 4. The method of claim 3, wherein the textsnippet is further selectable to display at least one of a date orauthor of the POI free-text review from which the displayed POI tip wasextracted.
 5. The method of claim 1, wherein the user interfacecomprises: a first user interface portion displaying the retrievedinformation other than the retrieved POI tips and the semanticcategories associated with each of the retrieved POI tips; and a seconduser interface portion interactively displaying the retrieved POI tipsand the semantic categories associated with each of the retrieved POItips.
 6. The method of claim 5, wherein the first user interface portionand the second user interface portion are concurrently displayed in theuser interface; and wherein, in the second user interface portion, theretrieved POI tips and the semantic categories associated with each ofthe retrieved POI tips are initially hidden until the second userinterface portion is selected.
 7. The method of claim 6, wherein theuser interface further comprises an interactive widget for receivingfeedback for one or more of the displayed tips within each of theassociated displayed semantic categories.
 8. The method of claim 7,wherein the tips within a semantic category are further sortable by arelevance score that is calculated at least partially based on thereceived feedback.
 9. The method of claim 1, further comprising:retrieving, using the processor, a plurality of POI free-text reviews;identifying tip occurrences in each of the retrieved plurality of POIfree-text reviews using the natural language processing model;associating the identified tip occurrences with the at least one storedsemantic category using the natural language processing model; andstoring the identified tip occurrences and their association with the atleast one stored semantic category to provide the extracted POI tips.10. The method of claim 1, wherein the natural language processing modelcomprises a neural network.
 11. The method of claim 9, furthercomprising, for each of the retrieved plurality of POI free-textreviews: preprocessing the POI free-text review to generate one or moreinput words; feeding each of the generated one or more input words intothe tip identifier model; wherein said associating comprises, for eachof the one or more input words fed into the tip identifier model:generating, by the tip identifier model, the prediction score for eachof the at least one stored semantic categories; and associating theinput word with one or more of the at least one stored semanticcategories based on the prediction score.
 12. The method of claim 1,wherein the displayed tips within each of the associated displayedsemantic categories are sortable by one or more of a review date/time ora relevance score based on the prediction score.
 13. The method of claim1, further comprising: receiving feedback for one or more of thedisplayed tips; calculating a relevance score for the one or more of thedisplayed tips at least partially based on the received feedback;selecting one or more of the identified tip occurrences based on thecalculated relevance score; generating augmented training data includingthe selected one or more tip occurrences; and retraining the tipidentifier model using the generated augmented training data.
 14. Asystem for providing information to a user relating to apoint-of-interest (POI), the system comprising: a communicationinterface; a back-end including a processor and a memory, the back-endcomprising: a knowledge base; a tip identifier module configured to: (i)retrieve a plurality of POI free-text reviews, which are reviews of POIsprovided in a free-text structure; (ii) identify tip occurrences in eachof the POI free-text reviews, using a natural language processing model,each identified tip occurrence having factual information relating to arespective POI, (iii) associate the identified tip occurrences with atleast one semantic category based on a predefined tip ontology using thenatural language processing model, where each semantic category of thepredefined tip ontology organizes POI tips by type, and (iv) store theidentified tip occurrences and their association with the at least onesemantic category in the knowledge base; and an information retrievalmodule configured to retrieve information from the knowledge baserelating to at least one POI in response to a received query, whereinthe retrieved information comprises a set of the identified tipoccurrences relating to the at least one POI and their respectiveassociated semantic categories; a front-end including the processor andmemory, the front-end comprising: a POI query interface moduleconfigured to receive the query via the communication interface; and aPOI detail interface module configured to generate and transmit a userinterface interactively displaying the retrieved POI tips and thesemantic categories associated with each of the retrieved POI tips,wherein the displayed POI tips are sorted by their associated displayedsemantic categories; wherein the tip identifier module preprocesses theretrieved POI free-text reviews to generate one or more input words;wherein the associating the identified tip occurrences with at least onesemantic category comprises: generating a prediction score for each ofthe at least one stored semantic categories for the one or more inputwords; and associating the one or more input words with one or more ofthe at least one stored semantic categories based on the predictionscore.
 15. The system of claim 14, wherein the retrieved informationfrom the information retrieval module comprises a set of matching POIscorresponding to the received query, the set of matching POIs includingthe at least one POI; wherein, for each of the at least one POI, theretrieved information from the information retrieval module furthercomprises standard POI information relating to the at least one POI;wherein the user interface generated by the POI detail interface modulefurther interactively displays the standard POI information in a firstuser interface portion of the user interface; wherein the user interfacegenerated by the POI detail interface module displays the retrieved POItips and the semantic categories associated with each of the retrievedPOI tips in a second user interface portion of the user interface inwhich the retrieved POI tips and the semantic categories associated witheach of the retrieved POI tips are initially hidden until the seconduser interface portion is selected.
 16. The system of claim 15, whereinthe front-end further comprises: a matching POI interface moduleconfigured to generate and transmit a matching POI user interfaceinteractively displaying at least a portion of the retrieved set ofmatching POIs, the displayed matching POIs being selectable; wherein thePOI detail interface module is configured to generate and transmit theuser interface for the at least one POI in response to the at least onePOI being selected in the matching POI user interface.
 17. The system ofclaim 16, wherein at least one of the first user interface portion, thesecond user interface portion, or the matching POI interactive userinterface is collapsible and expandable.
 18. The system of claim 16,wherein the back-end further comprises: a map service module configuredto generate a map interface comprising a visualization of one or more ofthe matching POIs in the matching POI user interface.
 19. The system ofclaim 15, wherein the second user interface portion further comprises,for each of the displayed POI tips, an interactive widget for receivingfeedback for the displayed POI tip; and wherein the tip identifiermodule is further configured to: calculate a relevance score for the oneor more of the displayed tips at least partially based on the receivedfeedback; select one or more of the identified tip occurrences based onthe calculated relevance score; generate augmented training dataincluding a selected one or more tip occurrences; and retrain thenatural language processing model using the generated augmented trainingdata.
 20. A non-transitory computer-readable storage medium havingexecutable instructions stored thereon that when executed cause aprocessor to perform a method comprising: receiving a query via acommunication network; retrieving, using a processor, information from adata storage relating to at least one POI based on the received query,wherein the retrieved information comprises a set of retrieved POI tipsrelating to factual information of the at least one POI, each of the setof retrieved POI tips being previously extracted from POI free-textreviews, which are reviews of POIs provided in a free-text structure,and respectively associated in the data storage with one or moresemantic categories based on a predefined tip ontology, where eachsemantic category of the predefined tip ontology organizes POI tips bytype; generating, using the processor, a user interface interactivelydisplaying the retrieved POI tips and the semantic categories associatedwith each of the retrieved POI tips, wherein the displayed POI tips aresorted by their associated displayed semantic categories; andtransmitting the generated user interface to a computing device of theuser via the communication network for display on the computing device;wherein the method further comprises: retrieving, using the processor, aplurality of POI free-text reviews; preprocessing each of the retrievedPOI free-text reviews to generate one or more input words; identifyingtip occurrences in each of the retrieved plurality of POI free-textreviews using a natural language processing model; associating theidentified tip occurrences with the at least one stored semanticcategory using the natural language processing model; and storing theidentified tip occurrences and their association with the at least onestored semantic category to provide the extracted POI tips; wherein saidassociating the identified tip occurrences with the at least one storedsemantic category comprises: generating, by a tip identifier model, aprediction score for each of the at least one stored semantic categoriesfor the one or more input words; and associating the one or more inputwords with one or more of the at least one stored semantic categoriesbased on the prediction score.
 21. A method for providing information toa user relating to a point-of-interest (POI), the method comprising:receiving a query via a communication network; retrieving, using aprocessor, information from a data storage relating to at least one POIbased on the received query, wherein the retrieved information comprisesa set of retrieved POI tips relating to factual information of the atleast one POI, each of the set of retrieved POI tips being previouslyextracted from POI free-text reviews, which are reviews of POIs providedin a free-text structure, and respectively associated in the datastorage with one or more semantic categories based on a predefined tipontology, where each semantic category of the predefined tip ontologyorganizes POI tips by type; generating, using the processor, a userinterface interactively displaying the retrieved POI tips and thesemantic categories associated with each of the retrieved POI tips,wherein the displayed POI tips are sorted by their associated displayedsemantic categories; and transmitting the generated user interface to acomputing device of the user via the communication network for displayon the computing device; wherein the retrieved POI tips relating tofactual information of the at least one POI are retrieved from among aset of stored POI tips relating respectively to each of multiple POIs;wherein each of the stored POI tips comprises one or more tokens and arepreviously extracted from among a larger set of tokens in a POIfree-text review from a plurality of the POI free-text reviews; whereineach of the set of stored POI tips is respectively associated in thedata storage by POI tip with one or more semantic categories in thepredefined tip ontology; wherein each of the semantic categories in thepredefined tip ontology is a category of the stored POI tips relating tomultiple POIs; and wherein each of the POI tips is further associated inthe data storage with a respective POI among the multiple POIs.